Do Companies Have To Adjust To AI Or Vice Versa?
14 September, 2022 / Articles
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One out of ten. That’s the number of businesses enjoying “significant benefits” from AI implementation globally. Although it may sound blunt, the truth is that several organizations have no idea about how to implement AI correctly. Unlike what many business executives may believe, AI is not just about the automation of business processes.
Enterprise AI implementation must be made with long-term data-driven strategies in mind. To ingrain the technology within the fabric of your business, you’ll need to clearly explore how your business and AI can align perfectly to maximize its potential for widening your ROI, revenue generation, growth and diversification. For that purpose, understanding the symbiotic relationship between AI and your enterprise is vital.
How Organizations Adjust to AI Implementation
To say that AI is prevalent in the global corporate ecosystem would be an understatement in today’s times. No less than 93% of businesses use AI in one form or another in 2021. As you can imagine, the implementation of enterprise AI transforms each facet of your organization. Therefore, enterprise AI adoption must be preceded by certain adjustments at different levels in your business. Such adjustments are generally made in terms of infrastructure, personnel and policies.
Making Investments in Technological Infrastructure
Massive, organization-wide transformation cannot float without businesses going about it holistically right from the onset. Failing to do so often leads to such initiatives falling through for most businesses. A lack of coherence is the main reason why up to 70% of major transformation projects fail in organizations.
One of the first adjustments to be made while attempting AI-driven transformation is moving business operations and applications from an offline environment to a digital one. Essentially, this involves making the necessary investments to acquire the tools, technological platforms, data scientists, developers, researchers, testers and others.
Data is the most important part of AI-driven transformation. As you may know, data is arguably the most powerful resource today and a major catalyst for the digital transformation of businesses. Possessing the machine learning tools that can carry out big data analysis will be integral to organization-wide enterprise AI implementation. Possessing the necessary tech infrastructure that can be used to harness the power of big data is useful for improved decision-making and business sustainability.
Now that the technology part is covered, arguably the most significant adjustment comes next—personnel training. Essentially, the employees in your organization must be trained to comfortably handle the new tools and technologies. However, apart from technological know-how, the entire workforce in organizations must possess data fluency.
Improving Data Fluency of Workforce
Data literacy and fluency are the main competitive differentiators for data-driven businesses. Data fluency is the ease with which every individual in an organization can understand, interpret the data analysis and dynamics related to their role and communicate it with managers and employees from other areas of the business. For example, if AI-driven predictive analytics indicates that demand for a certain product in a specific zone will rise, the production manager can communicate it expertly with the procurement department to increase the material inflows to that zone in the next purchase cycle. Such expertise over data is increasingly becoming a necessity for employees in nearly all organizations. As per a study, 90% of business strategies will enlist information analytics and data fluency as an essential competency by 2022 if all solutions are backed by data—which is an inevitability in the future. Expertise in data analytics is one of the most pressing requirements in businesses today.
Unfortunately, several businesses around the world lack the kind of data fluency that is conducive for enterprise AI implementation. It is predicted that up to 75 million jobs will be lost to digitization in the future. This means that a failure to improve data fluency can be catastrophic for your personnel. To avoid such a nightmarish scenario, as per a 2019 study, more than 89% of businesses were working on building data fluency. But how do you increase fluency? By identifying and addressing the data-based skill gaps through regular assessments and team meetings in organizations.
Personnel upskilling is a surefire way to gradually build data literacy and fluency in your organization. Upskilling involves training employees at each level and role in data analytics and interpretation. The training given to employees can be related to their respective roles or on a general basis. Gradual upskilling enables your employees to graduate from having rudimentary data knowledge to basic data literacy to high-level understanding to, ultimately, data fluency. By building data literacy, and eventually, fluency, your business can plug your employees’ skill gaps.
Several organizations have streamlined the process of building data fluency in their workforce. For example, Marks & Spencer has developed a retail data academy for the upskilling of its employees. Similarly, AT&T has recently pledged a billion dollars to upskill their more than 250,000-strong workforce over a ten-year period.
After making adjustments on human and technological levels, organizations will need to build new enterprise AI governance policies to smoothen the process of enterprise AI implementation and digital transformation.
Implementing Enterprise AI Governance Policies
It is worth remembering that unregulated AI is akin to a wild beast with algorithmic bias, the black box problem and explainability-related issues, creating a potentially catastrophic scenario in the future for organizations post-AI implementation. Making governance policies to uphold the ethics of AI and keep compliance-related issues in check is a significant adjustment that organizations need to make during enterprise AI implementation.
So, what are AI governance policies? And why are they needed?
They are a list of rules used during the development and implementation phases of enterprise AI. Even before AI-based tools and applications are fully introduced in your business, your governance frameworks must be in place. Businesses must use a hands-on approach while creating and implementing enterprise AI governance regulations so that AI issues related to compliance—with external AI regulations and stringent data protection rules such as EU’s GDPR—can be avoided.
AI governance involves policies related to data collection, sharing and usage. For example, data must only be collected for customers who provide consent. Additionally, that data cannot be shared or tampered with. Based on such regulations, machine learning algorithms can be developed.
Governance is useful to mitigate AI bias and uphold the main values of the technology—fairness, transparency, explainability, human-centricity and accountability.
On the surface, it may seem as if an enterprise has to make all the adjustments than AI when it comes to the co-existence of both entities. However, the adjustments AI needs to make complete this symbiotic relationship.
How AI Adjusts to Organizations
AI-based machines are needed to complement humans at a workplace and not supersede them. This means that AI is used extensively to plug the gaps left by human personnel. AI’s adjustment to organizations is mostly about how much human intervention is required in decision-making, implementation and data evaluation or assessment.
According to a BCG study, the level of human intervention in AI-based operations depends on shifting context, operations and certain scenarios. For instance, AI can play a role of a data evaluator if the stakes are high and there are not several context points for reference of the AI tool. In such a case, the machine learning-based tools only perform plain analysis of available data. On the other end of the spectrum, AI can play the role of an automator—meaning that enterprise AI-based tools can not only perform data analytics autonomously, they can also make decisions without the need for human intervention. In between these modes, AI also can play the roles of decision-making, providing insights and recommendations and using pattern recognition to uncover hidden strategic insights from data. In this way, AI shapeshifts according to its capabilities and the kind of task it is involved in.
Knowing about the enterprise-AI relationship allows organizations to clearly identify weak areas—whether those are in their technological infrastructure, their employees’ skills and abilities, their governance policies or their human-AI intervention. Understanding this relationship is necessary as it enables organizations to make adjustments in any way required to overcome such major digitization challenges.
